Manufacturing companies have an especially difficult time trying to continue production, keeping employees safe and ensuring efficiency.
Here are some steps organizations can take to ensure agility on the factory floor.
1. Increase worker safety by employing productivity tools to help workers reduce contact with equipment interfaces, maintain physical distance, and track risks
Now more than ever, it’s critical to enable successful, efficient remote workforces where teams can still stay connected and collaborate, as safely as possible. At Google Cloud, we see three key areas to focus on: effective communication, intelligent operations and worker health prioritization.
As workforces become more dispersed — with more remote workers at home and only skeleton crews remaining at factories — organizations must still ensure that employees can easily connect with one another. Teams need to participate in virtual team meetings, joining securely from their personal devices, often in an ad hoc manner. They must have access to technology that supports high-quality audio and video, whether it’s a live stream for up to 100,000 employees, an interactive team meeting for hundreds of people or urgent communications with a handful of colleagues.
To boost worker safety, organizations can invest in intelligent operations while running in an environment of reduced worker capacity and increased requirements for worker safety. Tools powered by artificial intelligence (AI) can allow workers to control building systems such as lights, or to interact with machinery using voice commands or gestures, to reduce the risk of physical contamination and transfer associated with switches, keyboards and touch screens.
Finally, managers now have to prioritize worker health in novel ways, such as considering the distance between workers and voluntary reporting of potential illness. At Google Cloud, we’re developing easy-to-use solutions based on open interoperability protocols, analytics and machine vision that help our customers remotely monitor, manage and maintain their operational technology and mitigate risk of health-related absenteeism. We want to ensure worker safety and reduce the number of manual labor needs in the factory, particularly for quality control processes and procedures.
2. Power automation and remote working, ultimately freeing workers from repetitive processes, re-assigning workers to distanced stations and monitoring machines remotely.
Powering automation with AI helps manufacturers increase operational efficiency and transparency across organizations with better monitoring, more precise and timely interventions and better quality control. Intelligent automation can enable factories to run with limited capacity, using visual inspection and remote machine monitoring. In this instance, artificial intelligence (AI) has proven to be particularly beneficial in helping to automate the visual quality control process. It can detect quality and conformance conditions 24x7 without in-person attendance, with increasing accuracy through machine learning (ML) and remote management.
Powering automation with AI can also free employees up from manual processes and create opportunities to upskill. For example, a worker who spends all of their time inspecting one small part of the assembly line can now work across multiple machines remotely and be upskilled to train equipment specific machine learning models. There is a precedent for this already, as workers who monitor quality are best positioned to help train ML models by imparting their subject matter expertise and experience.
3. Manage volatility in demand and supply by leveraging internal data and external signals and optimizing operations in real-time.
As we navigate the current global COVID-19 pandemic, we anticipate increased volatility both in demand and supply. On the demand side, we expect an overall decline due to a weakened economic climate. We also expect fluctuations at a micro-market level, driven by local regulations to help control COVID-19’s reach.
On the supply side, we’ll likely see continued disruption due to reduced workforce availability, with governments restricting the ability of non-essential workers. The potential to mitigate this disruption will depend on the ability to staff up and productively run manufacturing facilities — likewise on a very localized level. Delays in raw materials supply or dependent parts from Tier 1 or Tier 2 suppliers may also adversely impact the supply side. This will have a downstream domino effect, further delaying manufacturers from completing final assembly.
To manage this volatility, enterprises need to improve their foresight into demand and supply by putting their data to work at a very high granularity. Since they cannot rely solely on historical performance anymore, companies must leverage a combination of internal data and external influencing variables to improve forecast accuracy and inventory turns, and optimize their operations in real-time. Doing so can help organizations predict fluctuations in supply over the coming days. And AI-powered solutions can do this at scale.
AI can power forecasting and optimize logistics. It can predict disruptions in the short-term by leveraging data from external sources — such as weather, traffic, transportation cost, and competitive and raw material pricing — to remove the guesswork and provide some level of certainty during these uncertain times. Additionally, combining AI with data from multiple sources, including ERP systems, manufacturers can better predict demand and adapt their operations in real-time.
4. Reduce costs and explore direct-to-consumer sales via online ordering to mitigate the loss of business.
With the impact of COVID-19 leading to a reduced demand in many areas, manufacturers will need to adjust their cost position. Manufacturers must focus on strengthening their financial position by reducing costs and exploring direct-to-consumer sales via online ordering to mitigate loss of business. For example, hardware, software and managed service contracts are provisioned for peak capacity, rather than average or on-demand requirements. This represents a financial and ownership risk due to high initial CAPEX.
Manufacturers can explore cloud migration assessment and planning, focusing on identifying levers driving IT costs to create a total cost of ownership and ROI model. Organizations can work collaboratively with cloud providers to build a proposal which drives down costs, accounting for agility and the innovation needs of their organization.
Coping with legacy systems, siloed data and interoperability when implementing new technologies, systems, and processes is a significant challenge. For manufacturers wanting to digitally transform, the data needed to have the desired impact is very rarely in one location and connected.
For manufacturers with factories in multiple locations including multiple continents, the cloud is the way to get that data together and facilitate real-time collaboration. By aggregating that data, cloud computing can help manufacturers modernize systems, make smarter decisions, and create new business models.
Dominik Wee is Managing Director Manufacturing, Industrial and Transportation at Google Cloud.